Using common clinical data improves the prediction of abnormal glucose tolerance by the new criteria of impaired fasting glucose: Tehran lipid and glucose study

Diabetes Res Clin Pract. 2007 Sep;77(3):459-64. doi: 10.1016/j.diabres.2007.02.008. Epub 2007 Mar 9.

Abstract

Objective: To identify a subgroup of individuals with impaired fasting glucose (IFG) based on the new 2003 criteria that would most likely benefit from performance of oral glucose tolerance test.

Methods: A cross-sectional study was carried out in 1999-2001 in an Iranian urban population which enrolled 8766 individuals over 20 years. Fasting and 2-h plasma glucose was measured in all subjects after exclusion of diabetic subjects. Logistic regression and receiver operation characteristic (ROC) curve analysis were used to determine the independent clinical risk factors and their optimal cut-points associated with impaired glucose tolerance (IGT) and dysglycemia (IGT or diabetes).

Results: Application of the new criteria decreased positive likelihood ratio (LR+) of IFG for detecting IGT (from 6.68 to 3.86) or dysglycemia (from 9.90 to 4.46) but slightly improved their agreement (Kappa increased from 0.158 to 0.286 for IGT and 0.238 to 0.354 for dysglycemia). When the clinical data (age >45 years, BMI >28 kg/m(2) and systolic blood pressure >125 mm Hg) were added to the new criteria, the agreement of IFG with IGT and dysglycemia significantly improved (Kappa=0.470 and 0.574, respectively). This also increased the LR(+) to 14.5 and 17.4, respectively, for detecting IGT or dysglycemia.

Conclusion: The new IFG definition in combination with common clinical risk factors constitutes a group that most likely predicts IGT or dysglycemia and may be a target for which preventive strategies should be considered.

MeSH terms

  • Adult
  • Blood Glucose / analysis
  • Cross-Sectional Studies
  • Female
  • Glucose Intolerance / diagnosis*
  • Glucose Tolerance Test / standards*
  • Humans
  • Hyperglycemia / diagnosis
  • Iran
  • Likelihood Functions
  • Lipids / analysis
  • Logistic Models
  • Male
  • Middle Aged
  • Predictive Value of Tests*
  • ROC Curve
  • Risk Factors

Substances

  • Blood Glucose
  • Lipids